Short answer
For research that requires finding and synthesizing information from multiple online sources, start with Deep Research in ChatGPT. It is a research feature, not a model choice: it can work with uploaded files, public web sources, and eligible connected apps, and produce a documented report with citations or source links. For a quick question that needs current web information, use ChatGPT Search instead. Search brings current web information into a conversation; Deep Research is intended for gathering, analyzing, and synthesizing information across a more extensive research task. (OpenAI Academy; OpenAI Help Center)
If you mean the model itself, GPT-6 Pro, powered by GPT-6 Astra, is the documented option for difficult tasks and longer-running workflows. Treat that as a conditional fit for demanding analysis—not proof that it wins every research task or has the best research performance. (OpenAI Help Center)
For API research workflows, choose by workload: gpt-6-astra for demanding research and complex reasoning, or gpt-6.1-sol for complex work where balancing capability and cost matters. These are API model IDs, not ChatGPT picker labels. The API model documentation lists web search and file search support; confirm the selected model’s supported interface and tools in the current API documentation before deployment. (GPT-6 Astra model documentation; GPT-6.1 Sol model documentation)
ChatGPT model access and API model details and prices below reflect OpenAI documentation checked November 12, 2026. Access can depend on plan and workspace settings.
Choose by research task
| Your research task | Best starting point | Why |
|---|---|---|
| Find a current answer or check recent developments | ChatGPT Search | It brings current web information into a conversation. Open citations and check the original sources. In enterprise workspaces, an owner can enable or disable Search. (OpenAI Academy) |
| Compare a topic using many sources and produce a documented report | Deep Research | It is designed for multi-step research, can use eligible sources, and synthesizes findings into a documented report with citations or source links. (OpenAI Help Center) |
| Summarize papers or files you already have | A model suited to the analysis, with the files attached | If you only need to analyze supplied material, a general chat may be enough. Use Deep Research when you also need outside sources or a multi-step investigation. Deep Research can work with uploaded files. (OpenAI Help Center) |
| Work through difficult material or a long-running analysis | GPT-6 Pro, if available to you | OpenAI describes Pro as an option for difficult tasks and longer-running workflows. Model and thinking-level availability depends on plan and workspace settings. (OpenAI Help Center) |
| Run a research workflow repeatedly through the API | Astra or Sol, selected by workload | Astra is positioned for complex reasoning and research; Sol is an option when balancing capability and cost. Check the current model documentation for the tools and interface supported by your selected model. (GPT-6 Astra model documentation; GPT-6.1 Sol model documentation) |
Model versus research feature: what are you actually choosing?
A model generates and reasons over the information it receives. A research feature or tool helps find information, work through sources, or organize the research process. For many research questions, access to relevant sources and the ability to inspect them matter as much as model choice.
In ChatGPT, use Search to bring current web information into a conversation. Use Deep Research when the work involves multiple steps or sources and you want to guide the research plan or source selection. Deep Research can use the public web, uploaded files, and eligible connected apps. (OpenAI Academy; OpenAI Help Center)
For example, “What changed in this company’s latest earnings release?” is a good fit for Search and a short source check. “Compare the company’s last four earnings releases, identify changes in guidance, and give me a cited summary” is better suited to Deep Research. These are workflow recommendations, not a guarantee that an answer will be complete or correct.
Which ChatGPT model should you pick?
For difficult, longer-running analysis, GPT-6 Pro is a named option to consider if your account has access. OpenAI says GPT-6 Pro is powered by GPT-6 Astra and describes the Pro option as intended for difficult tasks and longer-running workflows. Model and thinking-level availability vary by plan and workspace. (OpenAI Help Center)
That is not a claim that GPT-6 Pro is a universal research winner. If the task mainly involves finding and combining outside sources, first choose the research workflow that fits: Search for a current web lookup, Deep Research for multi-step, multi-source work. If you are analyzing material you already have, attach it and choose a model suited to the complexity of the analysis.
Practical rule:
- Need a current, straightforward answer? Use Search.
- Need a multi-source report? Use Deep Research.
- Need to reason through difficult material you supplied? Consider GPT-6 Pro, if available to you; add Deep Research if you also need outside sources.
Best API model for research
For API work, choose based on workload—not by assuming ChatGPT model-picker names map directly to API IDs. OpenAI’s API documentation positions Astra for demanding work and Sol as an option for balancing capability and cost. (GPT-6 Astra model documentation; GPT-6.1 Sol model documentation)
| API model | When to consider it | Standard text-token price |
|---|---|---|
gpt-6-astra |
Demanding research, complex reasoning, and document creation. | $10 USD per 1 million input tokens; $50 USD per 1 million output tokens. (GPT-6 Astra model documentation) |
gpt-6.1-sol |
Complex work where balancing capability and cost matters. | $2 USD per 1 million input tokens; $10 USD per 1 million output tokens. (GPT-6.1 Sol model documentation) |
These are API token prices, not ChatGPT subscription prices. The model documentation lists web search and file search support, but confirm that your selected model supports the tools and API interface your implementation will use. Tool support alone does not provide a complete research workflow: your application still needs to decide how to search, select sources, pass material to the model, and handle results. Check the current catalog before deployment because model details and prices can change. (OpenAI API model documentation)
A simple way to evaluate API candidates
Before committing, run the same representative research task through two candidate models—for example, Astra and Sol. This is a suggested evaluation, not a benchmark result. Give each model the same prompt and source material, then compare:
- Whether important claims are supported by the supplied sources.
- Whether it distinguishes source evidence from its own inference.
- Whether the synthesis captures disagreement or missing evidence.
- Output quality, latency, and actual token cost for your workload.
If your application needs current facts, test the whole search-and-answer workflow, not just the model’s response to pasted text. Check source links and important claims before relying on the result.
Check the evidence, even when the answer has citations
Citations and source links make an answer easier to review; they do not prove that every claim is correct or that a cited page supports it. Open the original sources, check whether they address the claim, and look for missing context or disagreement. (OpenAI Academy)
Bottom line: for most multi-source research in ChatGPT, start with Deep Research; for current web lookups, use Search; and for demanding model-based analysis, consider GPT-6 Pro if it is available to you. For API deployments, choose Astra or Sol according to workload, verified tool compatibility, and budget.
Sources
GPT-6 and other models in ChatGPT